Multimedia Semantic Integrity Assessment Using Joint Embedding Of Images And Text

Real world multimedia data is often composed of multiple modalities such as an image or a video with associated text (e.g. captions, user comments, etc.) and metadata. Such multimodal data packages are prone to manipulations, where a subset of these modalities can be altered to misrepresent or repurpose data packages, with possible malicious intent. It is, therefore, important to develop methods to assess or verify the integrity of these multimedia packages. Using computer vision and natural language processing methods to directly compare the image (or video) and the associated caption to verify the integrity of a media package is only possible for a limited set of objects and scenes. In this paper, we present a novel deep learning-based approach for assessing the semantic integrity of multimedia packages containing images and captions, using a reference set of multimedia packages. We construct a joint embedding of images and captions with deep multimodal representation learning on the reference dataset in a framework that also provides image-caption consistency scores (ICCSs). The integrity of query media packages is assessed as the inlierness of the query ICCSs with respect to the reference dataset. We present the MultimodAl Information Manipulation dataset (MAIM), a new dataset of media packages from Flickr, which we make available to the research community. We use both the newly created dataset as well as Flickr30K and MS COCO datasets to quantitatively evaluate our proposed approach. The reference dataset does not contain unmanipulated versions of tampered query packages. Our method is able to achieve F1 scores of 0.75, 0.89 and 0.94 on MAIM, Flickr30K and MS COCO, respectively, for detecting semantically incoherent media packages.Comment: *Ayush Jaiswal and Ekraam Sabir contributed equally to the work in this pape

MGCT: Mutual-Guided Cross-Modality Transformer for Survival Outcome Prediction using Integrative Histopathology-Genomic Features

The rapidly emerging field of deep learning-based computational pathology has shown promising results in utilizing whole slide images (WSIs) to objectively prognosticate cancer patients. However, most prognostic methods are currently limited to either histopathology or genomics alone, which inevitably reduces their potential to accurately predict patient prognosis. Whereas integrating WSIs and genomic features presents three main challenges: (1) the enormous heterogeneity of gigapixel WSIs which can reach sizes as large as 150,000x150,000 pixels; (2) the absence of a spatially corresponding relationship between histopathology images and genomic molecular data; and (3) the existing early, late, and intermediate multimodal feature fusion strategies struggle to capture the explicit interactions between WSIs and genomics. To ameliorate these issues, we propose the Mutual-Guided Cross-Modality Transformer (MGCT), a weakly-supervised, attention-based multimodal learning framework that can combine histology features and genomic features to model the genotype-phenotype interactions within the tumor microenvironment. To validate the effectiveness of MGCT, we conduct experiments using nearly 3,600 gigapixel WSIs across five different cancer types sourced from The Cancer Genome Atlas (TCGA). Extensive experimental results consistently emphasize that MGCT outperforms the state-of-the-art (SOTA) methods.Comment: 7 pages, 4 figures, accepted by 2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2023

On Validity of Gyrokinetic Theory

We study the validity of gyrokinetic theory by examining the destruction of magnetic moment adiabatic invariant in the presence of fluctuations. Contrary to common assertions, it is shown for the first time that the gyrokinetic theory rests not only on the magnetic moment conservation, but also on the fact that the particle dynamics constitutes a boundary layer problem. For low frequency fluctuations, there exists a quantitative, frequency independent threshold below which the adiabaticity is preserved, allowing thereby the general validity of gyrokinetic theory. The adiabaticity threshold in the high frequency regime, however, depends sensitively on frequency, which questions the generalization of gyrokinetic equation to arbitrary frequencies. Further analyses suggest that it is not feasible to construct a reduced kinetic equation based on superadiabaticity

3D Hierarchical Refinement and Augmentation for Unsupervised Learning of Depth and Pose from Monocular Video

Depth and ego-motion estimations are essential for the localization and navigation of autonomous robots and autonomous driving. Recent studies make it possible to learn the per-pixel depth and ego-motion from the unlabeled monocular video. A novel unsupervised training framework is proposed with 3D hierarchical refinement and augmentation using explicit 3D geometry. In this framework, the depth and pose estimations are hierarchically and mutually coupled to refine the estimated pose layer by layer. The intermediate view image is proposed and synthesized by warping the pixels in an image with the estimated depth and coarse pose. Then, the residual pose transformation can be estimated from the new view image and the image of the adjacent frame to refine the coarse pose. The iterative refinement is implemented in a differentiable manner in this paper, making the whole framework optimized uniformly. Meanwhile, a new image augmentation method is proposed for the pose estimation by synthesizing a new view image, which creatively augments the pose in 3D space but gets a new augmented 2D image. The experiments on KITTI demonstrate that our depth estimation achieves state-of-the-art performance and even surpasses recent approaches that utilize other auxiliary tasks. Our visual odometry outperforms all recent unsupervised monocular learning-based methods and achieves competitive performance to the geometry-based method, ORB-SLAM2 with back-end optimization.Comment: 10 pages, 7 figures, under revie

Praseodymium Mid-Infrared Emission In AlF3-Based Glass Sensitized By Ytterbium

Broadband emission was obtained over 2.6 to 4.1 μm (Pr3+: 1G4→3F4, 3F3) in AlF3-based glass samples doped with different concentrations of praseodymium and 1 mol% ytterbium using a 976 nm laser pump. An efficient energy transfer process from Yb3+: 2F5/2 to Pr3+: 1G4 was analyzed through emission spectra and fluorescence lifetime values. The absorption and emission cross-sections were calculated by Füchtbauer-Ladenburg and McCumber theories and a positive gain can be obtained when P\u3e0.3. To the best of the authors’ knowledge, this work represents the first report of broadband mid-infrared emission of Pr3+ in an AlF3-based glass. The results show that praseodymium doped AlF3-based glass sensitized by ytterbium could be a promising candidate for fiber lasers operating in mid-infrared region

THE EFFECT OF AIR POLLUTION ON SOPHORA JAPONICA (LEGUMINOSAE) AND EULECANIUM GIGANTEUM (SHINJI) (HEMIPTERA: COCCOIDEA: COCCIDAE) IN URBAN AREAS IN CHINA

THE EFFECT OF AIR POLLUTION ON SOPHORA JAPONICA (LEGUMINOSAE) AND EULECANIUM GIGANTEUM (SHINJI) (HEMIPTERA: COCCOIDEA: COCCIDAE) IN URBAN AREAS IN CHINA. A study was made of the effect of two air pollutants (sulphur dioxide and lead) on the pest status of the soft scale Eulecanium giganteum (Shinji) and on the accumulation of sulphur and lead in the scale’s host tree, Sophora japonica, in three cities in China, namely Taiyuan, Yuci and Taigu. E. giganteum is a major pest of several tree species in many cities in China where air pollution can be high. This study showed a positive correlation between the level of the pollutants in the trees and the populations of the scale. The leaves absorbed and accumulated a greater amount of sulphur dioxide (SO2 - as sulphur) and lead (Pb) than the twigs, but the trends were the same in each, namely with high levels in these tissues in the Spring and early Autumn. It is concluded that E. giganteum can withstand high levels of pollutants, both within the host plant and in the environment, whereas its natural enemies may not. Key words: urban areas, biology, percentage parasitism, sap quality, pollution levels, Beijing utila, Microterys clauseni, Blastothrix sericea, Eucomys sasakii, Anisetus, Coccinellidae, Helicodinidae, Encyrtidae, Coccinella septempunctata, Harmonia axyridis, Chilocorus rubidus

Predicting the Influence of Climate on Grassland Area Burned in Xilingol, China with Dynamic Simulations of Autoregressive Distributed Lag Models

The influence of climate change on wildland fire has received considerable attention, but few studies have examined the potential effects of climate variability on grassland area burned within the extensive steppe land of Eurasia. We used a novel statistical approach borrowed from the social science literature—dynamic simulations of autoregressive distributed lag (ARDL) models—to explore the relationship between temperature, relative humidity, precipitation, wind speed, sunlight, and carbon emissions on grassland area burned in Xilingol, a large grassland-dominated landscape of Inner Mongolia in northern China. We used an ARDL model to describe the influence of these variables on observed area burned between 2001 and 2018 and used dynamic simulations of the model to project the influence of climate on area burned over the next twenty years. Our analysis demonstrates that area burned was most sensitive to wind speed and temperature. A 1% increase in wind speed was associated with a 20.8% and 22.8% increase in observed and predicted area burned respectively, while a 1% increase in maximum temperature was associated with an 8.7% and 9.7% increase in observed and predicted future area burned. Dynamic simulations of ARDL models provide insights into the variability of area burned across Inner Mongolia grasslands in the context of anthropogenic climate change

A Microbiome-Based Index for Assessing Skin Health and Treatment Effects for Atopic Dermatitis in Children.

A quantitative and objective indicator for skin health via the microbiome is of great interest for personalized skin care, but differences among skin sites and across human populations can make this goal challenging. A three-city (two Chinese and one American) comparison of skin microbiota from atopic dermatitis (AD) and healthy pediatric cohorts revealed that, although city has the greatest effect size (the skin microbiome can predict the originated city with near 100% accuracy), a microbial index of skin health (MiSH) based on 25 bacterial genera can diagnose AD with 83 to ∼95% accuracy within each city and 86.4% accuracy across cities (area under the concentration-time curve [AUC], 0.90). Moreover, nonlesional skin sites across the bodies of AD-active children (which include shank, arm, popliteal fossa, elbow, antecubital fossa, knee, neck, and axilla) harbor a distinct but lesional state-like microbiome that features relative enrichment of Staphylococcus aureus over healthy individuals, confirming the extension of microbiome dysbiosis across body surface in AD patients. Intriguingly, pretreatment MiSH classifies children with identical AD clinical symptoms into two host types with distinct microbial diversity and treatment effects of corticosteroid therapy. These findings suggest that MiSH has the potential to diagnose AD, assess risk-prone state of skin, and predict treatment response in children across human populations.IMPORTANCE MiSH, which is based on the skin microbiome, can quantitatively assess pediatric skin health across cohorts from distinct countries over large geographic distances. Moreover, the index can identify a risk-prone skin state and compare treatment effect in children, suggesting applications in diagnosis and patient stratification

A Novel Compressed Sensing Method for Magnetic Resonance Imaging: Exponential Wavelet Iterative Shrinkage-Thresholding Algorithm with Random Shift

Aim. It can help improve the hospital throughput to accelerate magnetic resonance imaging (MRI) scanning. Patients will benefit from less waiting time. Task. In the last decade, various rapid MRI techniques on the basis of compressed sensing (CS) were proposed. However, both computation time and reconstruction quality of traditional CS-MRI did not meet the requirement of clinical use. Method. In this study, a novel method was proposed with the name of exponential wavelet iterative shrinkagethresholding algorithm with random shift (abbreviated as EWISTARS). It is composed of three successful components: (i) exponential wavelet transform, (ii) iterative shrinkage-thresholding algorithm, and (iii) randomshift. Results. Experimental results validated that, compared to state-of-the-art approaches, EWISTARS obtained the leastmean absolute error, the leastmean-squared error, and the highest peak signal-to-noise ratio. Conclusion. EWISTARS is superior to state-of-the-art approaches

Recent advances in luminescence and lasing research in ZBYA glass

In the last few decades, fluoride glasses have attracted a growing interest due to their unique advantages compared to multi-component oxide glasses. Among them, the most studied and widely used were fluorozirconate glasses, represented by ZrF4–BaF2–LaF3–AlF3–NaF (ZBLAN) glasses. However, compared with ZBLAN glasses, a kind of fluorozirconate glass with the components ZrF4–BaF2–YF3–AlF3 (ZBYA) has higher thermal and chemical stability. In this paper, we first introduce the advantages of ZBYA glasses compared to ZBLAN glasses. Then we review and discuss recent advances in research on luminescence and lasing in ZBYA glass and fiber. These studies suggest that ZBYA glass has strong potential for use as a gain medium material in high power mid-infrared fiber lasers